Use of multivariate analysis to assess treatment approaches

ABSTRACT

Fisher discriminant analysis is performed on data sets of typically developing (TD) individuals and data sets of autism spectrum disorder (ASD) individuals to produce a model that classifies TD individuals from ASD individuals. The ASD data sets include pre-treatment folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathway metabolic profile data and post-treatment folate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathway metabolic profile data for patients receiving one or more ASD treatments. Changes in adaptive behavior are predicted by utilizing regression of changes in adaptive behavior and changes in biochemical measurements observed in the data sets. Thus, the system can be used to predict the effectiveness of a given course of treatment for an ASD patient based on measured metabolite data of that patient, or to predict the overall effectiveness of a clinical trial based on metabolite data for the trial participants.

CROSS REFERENCE TO RELATED APPLICATION(S)

This application is a national stage filing of International ApplicationNo. PCT/US2019/065642, filed Dec. 11, 2019, which claims the benefit ofU.S. Provisional Application Nos. 62/778,091, filed Dec. 11, 2018, and62/945,921, filed Dec. 10, 2019, which are incorporated by reference asif disclosed herein in their entirety.

BACKGROUND

Deficits in communication and behavior are defining characteristics ofautism spectrum disorder (ASD), a neurodevelopmental disorder estimatedby the Centers for Disease Control and Prevention to affect 1 out of 59children in the United States. The national economic burden of ASD in2015 was calculated to be $268 billion, similar to the costs of diabetesand attention deficit hyperactivity disorder. ASD is a highlyheterogeneous disorder in terms of how it presents itself in eachindividual, with as many as 95% of diagnosed children also affected byat least one co-occurring condition and regressive forms of the disordernot being uncommon. Despite the large body of research investigating theetiology of ASD, there is relatively limited understanding of thepathophysiology of the disorder aside from complex interactions betweengenetic and environmental contributors being involved.

As a result of the heterogeneity and lack of biological understanding ofASD, the current standards for diagnosis are clinical evaluations ofpatient behavior, which while comprehensive do not offer the objectiveassessment of ASD status that a biomarker can offer. A consequence ofthis gap in knowledge is initial ASD diagnoses being made at a medianage of four years even though stable diagnoses have been shown to bepossible at two years of age in a large percentage of children. Giventhat earlier behavioral intervention typically leads to milderASD-related symptoms and improved development of social and behavioralskills later in life, it is of great interest to achieve improvedmethods of ASD screening. Identification of biological markers fordiagnosing ASD or assessing ASD risk status would thus represent asignificant step towards improving long-term outcomes in individualswith ASD.

Potential biomarkers for ASD diagnosis may involve the folate-dependentone-carbon metabolism (FOCM) and transsulfuration (TS) pathways as thesepathways have been linked to metabolic abnormalities in ASD in severalstudies. Case-control studies show that markers of DNA methylation andintracellular redox status are significantly different in individualswith ASD compared to typically developing (TD) peers, suggestingperturbations both in the epigenetic control of gene expression and inthe control of intracellular oxidative stress. Subsequent studies havefound a strong ability to classify individuals as having ASD or beingTD, as well as predict adaptive behavior, based on these measurements.Development of a mathematical model of these pathways with parametersestimated from clinical data has also pointed to several metabolicreactions that may be disrupted in individuals with ASD.

Aside from investigating FOCM/TS metabolites for diagnostic purposes,correcting activity in the FOCM and TS pathways may affect underlyingbiological processes that contribute to ASD pathophysiology, thus makingmetabolic abnormalities in these pathways promising targets for clinicaltreatment. Further, it has been suggested that early detection ofmetabolic dysfunction to determine ASD risk and allowing for proactivetreatment strategies could potentially lead to practical interventionplans for at least a subset of those at risk for ASD. Since the aim oftreatment, however, is not just to correct metabolic abnormalities, butalso to alleviate the primary behavioral symptoms of ASD, it would be ofgreat value to determine treatment targets where improvements inmetabolic activity give rise to amelioration of observed behavior.Previous studies by the authors have investigated the effects oftreatment with methylcobalamin (MeCbl) in combination with low-dosefolinic acid (LDFA), tetrahydrobiopterin (BH₄), and high-dose folinicacid (HDFA) for improving metabolic and behavioral outcomes inindividuals with ASD. The growing body of literature describing theefficacy of these treatments suggests unique mechanisms by which eachacts upon metabolic pathways that may be dysfunctional in ASD.

MeCbl, one treatment option for ASD that has been explored, is acofactor for the methionine synthase enzyme that contributes to theprocess of DNA methylation. Levels of methionine synthase messenger RNAin the frontal cortex typically decrease with age, but this decrease hasbeen found to occur more quickly in ASD even though actual levels of theenzyme do not appear to be affected significantly. Concentrations ofMeCbl in the frontal cortex of children with ASD have been measured tobe three times lower than those in TD children, with an associatedthree-fold decrease in methionine synthase activity also measured. Ithas been suggested that cobalamin transporter polymorphisms andmutations may contribute to the development of ASD. Open-label anddouble-blind placebo-controlled studies of MeCbl treatment have observedimprovement in metabolism and ASD-related symptoms in children with thedisorder.

Another studied treatment for ASD involves BH₄, which has diverse rolesin monoamine neurotransmitter production, phenylalanine breakdown, andnitric oxide synthesis. Reduced cerebrospinal fluid levels of BH₄ havebeen reported in children with ASD, with one study reporting theselevels to be 42% of those found in TD children and a small open-labeltrial of BH₄ requiring deficient levels as an inclusion criterion.Analysis of genes related to BH₄ pathways has suggested that thesynthesis of BH₄ may be impaired in individuals with ASD. Onedouble-blind placebo-controlled study with BH₄ observed increases insocial interaction after six months of treatment, while a more recenttrial described significant improvements in ASD-related mannerisms,hyperactivity, inappropriate speech, and social awareness. Although itis unclear which underlying biological mechanisms are targeted by BH₄treatment, its therapeutic effect may derive from its correction ofoxidative stress and overall folate metabolism in the central nervoussystem.

Folinic acid is also a potential treatment for ASD and is anaturally-occurring form of folate, which is included in purine andpyrimidine productions, aids in the transfer of carbon during theprocess of amino acid synthesis, and contributes to DNA methylationprocesses. Early studies of folate deficiency in the central nervoussystem indicated a potential connection to cases of ASD and otherneurological deficits, with later studies also reporting increasedlevels of folate receptor autoantibodies in the blood to be correlatedwith the presentation of ASD-related symptoms and physiology.Additionally, higher rates of developmental deficits and ASD-likebehaviors have been observed in animal models administered folatereceptor antibodies during gestation and the pre-weaning period. The useof folate supplements during pregnancy may serve to combat thesedeleterious effects as it has been associated with a reduced risk of ASDin the child; this is likely due to folate's protective effect forproper neural tube development. Treatment with folinic acid has alsobeen found to correct certain abnormalities of the cerebrospinal fluidand improve ASD-related symptoms and behavior.

Even though a number of studies have tested the effect of treatment onindividually measured compounds or on certain behavioral measures inindividuals with ASD, there remains a need to study the effect of atreatment on combinations of metabolites of the FOCM/TS pathways and tocorrelate pathway-wide changes to shifts in behavioral measures.

SUMMARY

Accordingly, the present disclosure relates to methods and systems forassessing, comparing, and/or predicting the effectiveness of treatmentsfor disorders using multivariate statistical analysis. In oneembodiment, the method includes the steps of developing multivariatemodels of the effects of treatments on a condition and then using aregression analysis to predict changes in the condition as a result ofchanges in the treatment. In some embodiments, the system includes atleast one processor and a non-transitory computer storage media encodedwith one or more computer programs executed by the at least oneprocessor. In some embodiments, the one or more computer programs areconfigured to perform a multivariate statistical analysis on one or moredata sets of typically developing (TD) individuals and one or more datasets of autism spectrum disorder (ASD) individuals to produce a modelthat classifies TD individuals from ASD individuals, wherein the TD datasets include metabolic profile data for a plurality of TD individualsand ASD data sets include pre-treatment metabolic profile data andpost-treatment metabolic profile data for patients receiving one or moreASD treatments; calculate pre-treatment discriminant scores andpost-treatment discriminant scores for a plurality of patients in theASD data sets; identify a change from the pre-treatment discriminantscore to the post-treatment discriminant score to quantify a treatmenteffect on metabolic profiles of the plurality of patients; perform atreatment effect regression analysis on changes in adaptive behaviorscores and the metabolic profiles of the plurality of patients; andquantify a predicted adaptive behavior score change for a target patientfrom a measured metabolic profile change in the target patient, whereinthe target patient is undergoing at least one of the one or more ASDtreatments. In some embodiments, the one or more computer programsconfigured to identify a change from the pre-treatment discriminantscore to the post-treatment discriminant score to quantify a treatmenteffect on metabolic profiles of the plurality of patients is furtherconfigured to calculate, for the plurality of patients in the ASD datasets, a first probability that the model will classify a data setpatient as TD pre-treatment and a second probability that the model willclassify the data set patient as TD post-treatment; and identify achange from the first probability to the second probability. In someembodiments, an increase in probability from the first probability tothe second probability identifies a positive treatment effect.

In some embodiments, the metabolic profile data includes data forfolate-dependent one-carbon metabolism (FOCM) metabolites,transsulfuration (TS) pathway metabolites, methionine, SAM, SAH,SAM/SAH, 8-OHG, adenosine, homocysteine, cysteine,γ-L-glutamyl-L-cysteine (Glu.-Cys.), L-cysteine-L-glycine (Cys.-Gly.),tGSH, fGSH, GSSG, fGSH/GSSG, tGSH/GSSG, chlorotyrosine, nitrotyrosine,tyrosine, tryptophane, fCystine, fCysteine, fCystine/fCysteine, apercent of DNA methylation, a percent of oxidized glutathione, orcombinations thereof. In some embodiments, the multivariate statisticalanalysis includes performing a Fisher discriminant analysis. In someembodiments, the one or more ASD treatments include MeCbl+LDFA, BH₄,HDFA, or combinations thereof. In some embodiments, the one or more ASDtreatments include placebo treatments. In some embodiments, the adaptivebehavior score includes a Vineland Adaptive Behavior Scales (VABS)Composite score. In some embodiments, the treatment effect regressionanalysis includes use of a kernel partial least squares algorithm.

Some embodiments of the present disclosure include a computerimplemented method to predict changes in adaptive behavior includingperforming a multivariate statistical analysis on one or more data setsof TD individuals and one or more data sets of ASD individuals toproduce a model that classifies TD individuals from ASD individuals,wherein the TD data sets include metabolic profile data for a pluralityof TD individuals and ASD data sets include pre-treatment metabolicprofile data and post-treatment metabolic profile data for patientsreceiving one or more ASD treatments; calculating pre-treatmentdiscriminant scores and post-treatment discriminant scores for aplurality of patients in the ASD data sets; identifying a change fromthe pre-treatment discriminant score to the post-treatment discriminantscore to quantify a treatment effect on metabolic profiles of theplurality of patients; performing a treatment effect regression analysison changes in adaptive behavior scores and metabolic profiles of theplurality of patients; and quantifying a predicted adaptive behaviorscore change for a target patient from a measured metabolic profilechange in the target patient, wherein the target patient is undergoingat least one of the one or more ASD treatments.

Some embodiments of the present disclosure include a computerimplemented method to predict changes in adaptive behavior includingperforming Fisher discriminant analysis on one or more data sets of TDindividuals and one or more data sets of ASD individuals to produce amodel that classifies TD individuals from ASD individuals, wherein theTD data sets include FOCM and TS pathway metabolic profile data for aplurality of TD individuals and ASD data sets include pre-treatment FOCMand TS pathway metabolic profile data and post-treatment FOCM and TSpathway metabolic profile data for patients receiving one or more ASDtreatments; calculating, for the plurality of patients in the ASD datasets, a first probability that the model will classify a data setpatient as TD pre-treatment and a second probability that the model willclassify the data set patient as TD post-treatment; identifying a changefrom the first probability to the second probability to quantify atreatment effect on metabolic profiles of the plurality of patients;performing a treatment effect regression analysis on changes in adaptivebehavior scores and metabolic profiles of the plurality of patients; andquantifying a predicted adaptive behavior score change for a targetpatient from a measured metabolic profile change in the target patient,wherein the target patient is undergoing at least one of the one or moreASD treatments.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings show embodiments of the disclosed subject matter for thepurpose of illustrating the invention. However, it should be understoodthat the present application is not limited to the precise arrangementsand instrumentalities shown in the drawings, wherein:

FIG. 1 is schematic drawing of a system for predicting changes inadaptive behavior according to some embodiments of the presentdisclosure;

FIG. 2A is a chart of a method for predicting changes in adaptivebehavior according to some embodiments of the present disclosure;

FIG. 2B is a chart of a method for predicting changes in adaptivebehavior according to some embodiments of the present disclosure;

FIG. 3A is a graph of classification results for a Fisher DiscriminantAnalysis model according to some embodiments of the present disclosure;

FIG. 3B is a graph of regression results for a regression modelaccording to some embodiments of the present disclosure;

FIG. 4A portrays classification results for a Fisher DiscriminantAnalysis model according to some embodiments of the present disclosure;

FIG. 4B portrays classification results for a Fisher DiscriminantAnalysis model according to some embodiments of the present disclosure;and

FIG. 5 is a graph portraying data concerning relative frequency ofmetabolites in exemplary models consistent with some embodiments of thepresent disclosure.

DETAILED DESCRIPTION

Referring now to FIG. 1, some embodiments of the present disclosure aredirected to a system 100 to predict changes in adaptive behavior. Thesystem 100 includes a non-transitory computer storage media 102 encodedwith one or more computer programs 104 executed by at least oneprocessor 106.

In some embodiments, non-transitory computer storage media 102 includesa classifier 108. In some embodiments, classifier 108 can be any script,file, program, application, set of instructions, or computer-executablecode, that is configured to enable a computing device on which theclassifier 108 is executed to classify incoming data sets into one of atypically developing (TD) class and autism spectrum disorder (ASD)class.

In some embodiments, non-transitory computer storage media 102 includesa scoring engine 110. In some embodiments, system 100 includes a memory112. In some embodiments, system 100 includes class templates 114 anddata sets 116. In some embodiments, class templates 114 and data sets116 are stored on the memory 112. In some embodiments, memory 112 islocal, remote, or combinations thereof. In some embodiments, system 100includes a communication module 118 configured to send and receivecommunications, data, etc. In some embodiments, communication module 118is configured to receive a test data set 120 and communicate the testdata set to non-transitory computer storage media 102 (not pictured).

In some embodiments, classifier 108 generates class template 114 fordata sets 116. In some embodiments, classifier 108 generates the classtemplate 114 using Fisher Discriminant Analysis (FDA). FDA can maximizedifferences between multiple classes. In some embodiments, classifier108, using FDA, determines a linear combination of the values in each ofthe data sets 116 that projects the data sets onto a line joining themean of the ASD and TD groups. In some embodiments, classifier 108calculates the linear combination such that the linear combinationprojects the data sets 116 associated with the same class near oneanother and data sets 116 associated with the other class disparately.For example, classifier 108 can calculate a linear combination thatprojects the data sets 116 into a TD class and an ADS class. In someembodiments, classifier 108 saves the linear combination as a classtemplate 114. In some embodiments, classifier 108 also determines athreshold that separates the two classes.

In some embodiments, classifier 108 uses nonlinear techniques toclassify data sets 116 into the ASD class and the TD class. For example,classifier 108 can use kernel partial least squares (KPLS) to classifythe data sets. Kernel techniques provide general nonlinear extensions tothe popular linear partial least squares (PLS) regression. The KPLSalgorithm commences by defining a nonlinear transformation f=ψ(x) on thepredictor set x. In some embodiments, ψ(x) is a Guassian kernel. In someembodiments, rather than regress x as a linear PLS, y can be regressedonto the higher dimensional feature space f.

As discussed above, in some embodiments, classifier 108 generates classtemplates 114 based on training data that includes data sets 116 fromboth ADS and TD patients. In some embodiments, data sets 116 (and theincoming test data set 120) are each a vector that includes a pluralityof values. In some embodiments, a plurality of the values represents ametabolite concentration. In some embodiments, the metaboliteconcentrations stored in the data sets 116 are the concentration of atleast one of folate-dependent one-carbon metabolism (FOCM) metabolites,transsulfuration (TS) pathway metabolites, methionine, SAM, SAH,SAM/SAH, 8-OHG, adenosine, homocysteine, cysteine,γ-L-glutamyl-L-cysteine (Glu.-Cys.), L-cysteine-L-glycine (Cys.-Gly.),tGSH, fGSH, GSSG, fGSH/GSSG, tGSH/GSSG, chlorotyrosine, nitrotyrosine,tyrosine, tryptophane, fCystine, fCysteine, fCystine/fCysteine, apercent of DNA methylation, a percent of oxidized glutathione, orcombinations thereof. In some embodiments, data sets 116 includes avalue for each of the above metabolites. In some embodiments, data sets116 can include a value for a sub-population of the metabolites. In someembodiments, the order of the metabolite concentrations are arranged inthe same order in each of the data sets 116. In some embodiments, testdata set 120 includes a value for each metabolite in data sets 116. Insome embodiments, test data set 120 includes a value for substantiallyall metabolites in data sets 116.

As discussed above, in some embodiments, non-transitory computer storagemedia 102 includes the scoring engine 110. In some embodiments, scoringengine 110 is any script, file, program, application, set ofinstructions, or computer-executable code, that is configured to enablea computing device on which the scoring engine 106 is executed toconvert a data set into a score, which is used as a biomarker tocategorize the data set, e.g., data set 116 and/or test data set 120,into a TD class or ASD class. In some embodiments, upon receiving testdata set 120, scoring engine 110 retrieves class template 114 from thememory 112 and calculates a score for test data set 120 based on thelinear combination stored in the class template 114. In someembodiments, scoring engine 110 compares the calculated score to thethreshold to determine if the test data set 120 should be associatedwith the TD class or the ASD class.

In some embodiments, as discussed above, non-transitory computer storagemedia 102 is encoded with one or more computer programs 104 executed byat least one processor 106. In some embodiments, one or more computerprograms 104 are configured to perform a multivariate statisticalanalysis on one or more data sets of ASD individuals 116A and one ormore data sets of TD individuals 116B and to produce a model, e.g.,classifier 108, that classifies TD individuals from ASD individuals. Asdiscussed above, in some embodiments, the multivariate statisticalanalysis includes performing a Fisher discriminant analysis. In someembodiments, TD data sets 116B include metabolic profile data for aplurality of TD individuals and ASD data sets 116A include pre-treatmentmetabolic profile data and post-treatment metabolic profile data forpatients receiving one or more ASD treatments. In some embodiments, theone or more ASD treatments include any treatments whose effect is toadjust local or systemic levels of a given metabolite in a targetpatient. In some embodiments, the one or more ASD treatments includeadministration of MeCbl+LDFA, BH₄, HDFA, or combinations thereof. Insome embodiments, the one or more ASD treatments further include placebotreatments. As discussed above, in some embodiments, the metabolicprofile data includes data for FOCM metabolites, TS pathway metabolites,methionine, SAM, SAH, SAM/SAH, 8-OHG, adenosine, homocysteine, cysteine,γ-L-glutamyl-L-cysteine (Glu.-Cys.), L-cysteine-L-glycine (Cys.-Gly.),tGSH, fGSH, GSSG, fGSH/GSSG, tGSH/GSSG, chlorotyrosine, nitrotyrosine,tyrosine, tryptophane, fCystine, fCysteine, fCystine/fCysteine, apercent of DNA methylation, a percent of oxidized glutathione, orcombinations thereof.

In some embodiments, one or more computer programs 104 are configured tocalculate pre-treatment discriminant scores and post-treatmentdiscriminant scores for a plurality of patients in ASD data sets 116A.e.g., via scoring engine 110. In some embodiments, one or more computerprograms 104 are configured to identify a change from the pre-treatmentdiscriminant score to the post-treatment discriminant score to quantifya treatment effect on metabolic profiles of the plurality of patients.In some embodiments, one or more computer programs 104 are configured tocalculate a probability distribution function (PDF) of the pre-treatmentdiscriminant scores and the post-treatment discriminant scores. In someembodiments, one or more computer programs 104 are configured tocalculate, for the plurality of patients in the ASD data sets, a firstprobability that the model will classify a data set patient as TDpre-treatment and a second probability that the model will classify thedata set patient as TD post-treatment. In some embodiments, one or morecomputer programs 104 are configured to identify a change from the firstprobability to the second probability. The null hypothesis, H₀, forclassification states that a participant belongs to the TD group. Withthis hypothesis, the Type I (false positive) error is the probability ofincorrectly classifying a TD individual as having ASD. The Type II(false negative) error is then the probability of incorrectlyclassifying a participant with ASD as being TD. In some embodiments, thechange in Type II error yielded by a certain treatment, e.g., increasedprobability of being classified as a TD individual after treatment, wasused to quantify the abilities of these treatments to shift themetabolic profiles of individuals with ASD to be more, or less, similarto those of the TD class, thus quantifying a treatment effect onmetabolic profiles of the plurality of patients. As will be discussed ingreater detail below, through evaluation of the FDA model on theclinical trial data sets, the MeCbl+LDFA treatment was found to providethe greatest correction in ASD-related metabolic abnormalities, with theeffects of BH₄ just slightly smaller; both of these treatments increasedthe rate of ASD misclassification by more than 40% each.

In some embodiments, one or more computer programs 104 are configured toperform a treatment effect regression analysis on changes in adaptivebehavior scores and the metabolic profiles of the plurality of patients.In some embodiments, the treatment effect regression analysis includesuse of a kernel partial least squares algorithm. As a result, one ormore computer programs 104 are configured to predict an adaptivebehavior score change for a target patient undergoing at least one ofthe one or more ASD treatments, e.g., that of test data set 120, from ameasured metabolic profile change in the target patient. In someembodiments, the adaptive behavior score includes a Vineland AdaptiveBehavior Scales (VABS) Composite score. In some embodiments, one or morecomputer programs 104 are configured to quantify and/or output thepredicted adaptive behavior score change.

In some embodiments, system 100 is used to predict the effectiveness ofa given course of treatment for an ASD patient based on measuredmetabolite data of that patient. In some embodiments, system 100 is usedto predict the overall effectiveness of a clinical trial based onmetabolite data for the trial participants.

Additional background and supporting disclosure concerning themultivariate statistical analysis of the present disclosure can be foundin Howsmon, D. P., Kruger, U., Melnyk, S., James, S. J., and Hahn, J.(2017). “Classification and adaptive behavior prediction of childrenwith autism spectrum disorder based upon multivariate data analysis ofmarkers of oxidative stress and DNA methylation.” PLOS Comput. Biol. 13,e1005385, and US/2018/0358127, each of which are incorporated herein byreference in their entireties.

Referring now to FIG. 2A, some embodiments of the present disclosure aredirected to a computer implemented method 200A for predicting changes inadaptive behavior. At 202A, a multivariate statistical analysis isperformed on one or more data sets of TD individuals and one or moredata sets of ASD individuals to produce a model that classifies TDindividuals from ASD individuals. As discussed above, in someembodiments, the TD data sets include metabolic profile data for aplurality of TD individuals. In some embodiments, the ASD data setsinclude pre-treatment metabolic profile data and post-treatmentmetabolic profile data for patients receiving one or more ASDtreatments. In some embodiments, the metabolic profile data includesdata for FOCM metabolites, TS pathway metabolites, methionine, SAM, SAH,SAM/SAH, 8-OHG, adenosine, homocysteine, cysteine,γ-L-glutamyl-L-cysteine (Glu.-Cys.), L-cysteine-L-glycine (Cys.-Gly.),tGSH, fGSH, GSSG, fGSH/GSSG, tGSH/GSSG, chlorotyrosine, nitrotyrosine,tyrosine, tryptophane, fCystine, fCysteine, fCystine/fCysteine, apercent of DNA methylation, a percent of oxidized glutathione, orcombinations thereof. In some embodiments, the multivariate statisticalanalysis comprises performing a Fisher discriminant analysis. In someembodiments, the one or more ASD treatments include MeCbl+LDFA, BH₄,HDFA, or combinations thereof. In some embodiments, the one or more ASDtreatments include placebo treatments.

At 204A, pre-treatment discriminant scores and post-treatmentdiscriminant scores are calculated for a plurality of patients in theASD data sets. At 206A, a change from the pre-treatment discriminantscore to the post-treatment discriminant score is identified to quantifya treatment effect on metabolic profiles of the plurality of patients.In some embodiments, identifying step 206A includes calculating, for theplurality of patients in the ASD data sets, a first probability that themodel will classify a data set patient as TD pre-treatment and a secondprobability that the model will classify the data set patient as TDpost-treatment, and identifying a change from the first probability tothe second probability. As discussed above, and without wishing to bebound by theory, an increase in probability from the first probabilityto the second probability identifies a positive treatment effect.

At 208A, a treatment effect regression analysis on changes in adaptivebehavior scores and metabolic profiles of the plurality of patients isperformed. As discussed above, in some embodiments, the adaptivebehavior score includes a Vineland Adaptive Behavior Scales (VABS)Composite score. In some embodiments, the treatment effect regressionanalysis includes use of a kernel partial least squares algorithm. At210A, a predicted adaptive behavior score change is quantified for atarget patient from a measured metabolic profile change in the targetpatient who is undergoing at least one of the one or more ASDtreatments.

Referring now to FIG. 2B, some embodiments of the present disclosure aredirected to a computer implemented method 200B to predict changes inadaptive behavior. At 202B, Fisher discriminant analysis is performed onone or more data sets of TD individuals and one or more data sets of ASDindividuals to produce a model that classifies TD individuals from ASDindividuals, wherein the TD data sets include FOCM) and TS pathwaymetabolic profile data for a plurality of TD individuals and ASD datasets include pre-treatment FOCM and TS pathway metabolic profile dataand post-treatment FOCM and TS pathway metabolic profile data forpatients receiving one or more ASD treatments. At 204B, a firstprobability that the model will classify a data set patient as TDpre-treatment and a second probability that the model will classify thedata set patient as TD post-treatment are calculated for the pluralityof patients in the ASD data sets. At 206B, a change from the firstprobability to the second probability is identified to quantify atreatment effect on metabolic profiles of the plurality of patients. At208B, a treatment effect regression analysis is performed on changes inadaptive behavior scores and metabolic profiles of the plurality ofpatients. At 210B, a predicted adaptive behavior score change isquantified for a target patient from a measured metabolic profile changein the target patient, wherein the target patient is undergoing at leastone of the one or more ASD treatments.

EXAMPLES

Description of Data Sets. Four data sets describing plasma FOCM/TSmeasurements from previous separately investigated and published studieswere obtained. The recommendations of the respective InstitutionalReview Boards (IRBs) described below were followed, with study protocolsalso approved by the respective IRBs. Written informed consent wasprovided by parents of study participants and assent was given byparticipants themselves, when appropriate, in accordance with theDeclaration of Helsinki.

Case-Control Data. Case-control data from the Integrated Metabolic andGenomic Endeavor (IMAGE) study at Arkansas Children's Research Institutewas also used. The case-control group consisted of children between 3and 10 years of age with a diagnosis of autistic disorder according tothe Diagnostic and Statistical Manual of Mental Disorders, FourthEdition (DSM-IV), the Autism Diagnostic Observation Schedule, and/or theChildhood Autism Ratings Scales (score greater than 30). TD controlswere age-matched and had no indications of behavioral or neurologicaldisorders as reported by their parents. In the ASD cohort, 85% ofparticipants were male while 48% of the TD cohort were male. Theprotocol for this study was approved by the IRB at the University ofArkansas for Medical Sciences in Little Rock, Ark.

MeCbl+LDFA Treatment Data. Subcutaneously injected MeCbl (75 μg/kg, onceevery three days) in combination with oral LDFA (400 μg, twice per day)was given to children with autism in a 12-week open-label trial.Included children were aged 2 to 7 years and met the diagnostic criteriafor autism as defined by the DSM-IV in addition to having a ChildhoodAutism Rating Scales score greater than 30. Boys and girls made up 82%and 18% of participants in this study, respectively. The IRB at theUniversity of Arkansas for Medical Sciences approved the protocol forthis study.

BH₄ Treatment Data. A 16-week open-label trial investigated the effectsof orally administered BH₄ (20 mg/kg, once per day) in children aged 2to 6 years old with a previous diagnosis of ASD that was confirmed atthe time of evaluation with DSM-IV criteria. Included children alsoneeded to exhibit social or language delays and have normalconcentrations of BH₄ in their cerebrospinal fluid. Study participantswere 90% males. Approval for this study was given by the IRB at theUniversity of Texas Health Science Center at Houston, Tex. FOCM/TSmarkers were measured at 8 and 16 weeks following the onset of treatmentin this trial; to maintain consistency with the other trials wheremarkers were measured after 12 weeks, the averages of the measurementstaken at 8 and 16 weeks were used.

HDFA Treatment Data. A double-blind placebo-controlled trial of HDFA (2mg/kg per day up to a maximum of 50 mg daily, given orally) wasadministered over 12 weeks to children between 3 and 14 years of age.ASD diagnoses were made using the Autism Diagnostic Observation Scheduleand/or Autism Diagnostic Interview—Revised, or by agreement betweenphysician, psychologist, and speech therapist, or by a physician'sdiagnosis according to the Diagnostic and Statistical Manual of MentalDisorders, Fifth Edition with later confirmation by the investigators.All children were required to have documented language impairment. 78%of the treatment group were male while 80% of the placebo group weremale. The protocol was approved by the IRB at the University of Arkansasfor Medical Sciences. All data for participants receiving a placebo inthe current analysis were provided.

Biochemical Measurements. Concentrations and ratios of metabolites inthe FOCM and TS pathways were measured in each of the above studies,with fifteen measurements appearing in all four data sets. Six of thesemeasures were associated with DNA methylation: methionine,S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH), the SAM/SAHratio (an indicator of DNA methylation capacity), homocysteine, andadenosine. The remaining nine measures were precursors of glutathione ormarkers of redox metabolism: total cysteine, glutamylcysteine (Glu-Cys),cysteinylglycine (Cys-Gly), total and free reduced glutathione (tGSH andfGSH, respectively), oxidized glutathione (GSSG), the ratios of totaland free reduced glutathione to oxidized glutathione (tGSH/GSSG andfGSH/GSSG, respectively; these are indicators of intracellular oxidativestress), and percent oxidized glutathione (a derived measure calculatedas 2GSSG/[GSH+2GSSG]).

Adaptive Behavior Assessment. The Vineland Adaptive Behavior scales(VABS) were used in all of the above studies to measure adaptivebehavior in the communication, daily living, and social subdomains. TheVABS Composite score incorporates these subdomains to provide a singlemeasure of adaptive behavior. Higher scores indicate better developmentof adaptive behavior.

Inclusion Criteria. Participants of the IMAGE study were included in thecurrent analysis if they had a complete panel of the fifteen FOCM/TSmarkers of interest. 92 participants with ASD and 82 TD controls metthis criterion and were thus considered for further analysis.Participants of the clinical trials were included if they had completepre- and post-treatment measurements for these fifteen markers inaddition to pre- and post-treatment VABS Composite scores. Meeting thesecriteria were 33 participants receiving MeCbl+LDFA, 8 participantsreceiving BH₄, 14 participants receiving HDFA, and 19 participantsreceiving a placebo (74 participants with ASD in total), and summarizedin Table 1 below.

TABLE 1 Participant numbers from the four data sets used TD InclusionStudy ASD Participants Participants Criteria IMAGE Case-l 92 82 Completepanel Contro of FOCM/TS MeCbl + LDFA 33  0 Complete pre- Trial andtreatment BH₄ Trial  8  0 panel of HDFA Trial 14 (+19 placebo)  0FOCM/TS Placebo* 19  0 VABS Composite

Multivariate Statistical Analysis. The analytical techniques employed bythis embodiment were coded in MATLAB. All data used for model trainingwere normalized such that each FOCM/TS marker had a mean of zero and astandard deviation of one across all training samples. Model validationsamples were then normalized according to the mean/standard deviationparameters used for normalization of the training data.

Fisher Discriminant Analysis. Individuals of the IMAGE study wereseparated into ASD and TD cohorts using Fisher discriminant analysis(FDA). FDA used the data matrix X of size n×m as input, where n studyparticipants are each defined by m biochemical measurements. Sampleinformation for study participant i was contained in row vector x_(i)(size 1×m) and that participant's value for measurement j is indicatedby x_(i,j). The input matrix X were considered as two matrices X_(ASD)and X_(TD) taken to represent the separate samples for the ASD and TDcohorts, respectively, with X_(ASD) composed of n_(ASD) samples andX_(TD) having n_(TD) samples. For the two-class problem presented here,FDA defined the between-class scatter matrix S_(B) (size m×m) as

S _(B) =n _(ASD)( x _(ASD) −x )( x _(ASD) −x )^(T) +n _(TD)( x _(TD) −x)( x _(TD) −x )^(T)

where x _(ASD) denotes the mean vector of samples in XASD, x _(TD)represents the mean vector among samples in X_(TD), and x indicates themean vector across all samples in X. The within-class scatter matrixS_(W) (size m×m) was then defined as

$S_{w} = {{n_{ASD}{\sum\limits_{i = 1}^{n_{ASD}}{( {x_{i} - {\overset{\_}{x}}_{ASD}} )( {x_{i} - {\overset{\_}{x}}_{ASD}} )^{T}}}} + {n_{TD}{\sum\limits_{i = 1}^{n_{TD}}{( {x_{i} - {\overset{\_}{x}}_{TD}} )( {x_{i} - {\overset{\_}{x}}_{TD}} )^{T}}}}}$

where x_(i) represents an individual sample from either the ASD or TDcohort. Using this information, FDA determined the m×1 projection vectorw that satisfies the objective function FDA determines the m×1projection vector w that satisfies the objective function

$ {\max\limits_{w}\frac{w^{T}S_{B}w}{w^{T}S_{w}w}}arrow{Jw}  = {S_{w}^{- 1}S_{B}w}$

where the optimal solution is given by the eigenvector of the matrixproduct S_(W) ⁻¹S_(B). A final discriminant score t_(i), which is theprojection of the ith data point onto the projection vector w, was givenby

t _(i) =x _(i) ·w=x _(i,1) w ₁ +x _(i,2) w ₂ + . . . +x _(i,m) w _(m).

Kernel Density Estimation. Kernel density estimation assumed thatsamples not included in the estimation of a PDF will likely be near thereference samples that were used. As part of the estimation procedure, aGaussian kernel function was centered on each reference sample; the sumof the kernel functions associated with the samples of a particularcohort was then taken to be representative of that cohort's total PDF.

Null Hypothesis for Classification. As discussed above, the nullhypothesis, H₀, for classification states that a participant belongs tothe TD group. With this hypothesis, the Type I (false positive) error isthe probability of incorrectly classifying a TD participant as havingASD. The Type II (false negative) error is then the probability ofincorrectly classifying a participant with ASD as being TD. Theseerrors' magnitudes are dictated by the choice of the discriminant scorethreshold for H₀ and the amount of overlap between the PDFs for the twocohorts. In order to balance the Type I and Type II errors, thisanalysis placed the threshold H₀ at the point where the absolutedifference between these errors in the fitted model was minimized.

FDA Model Evaluation of Treatment Data. Using the FDA model identifiedfrom the IMAGE data and based on the same subset of FOCM/TSmeasurements, pre- and post-treatment discriminant scores werecalculated for individuals with ASD who received the MeCbl+LDFA, BH₄,HDFA, and placebo treatments. Pre- and post-treatment Type II errorswith respect to H₀, which was previously determined from model fittinginvolving data from the IMAGE study, were then computed for theestimated PDFs of pre- and post-treatment discriminant scores(separately for each treatment). Referring now to FIGS. 3A-3B, thechange in Type II error yielded by each treatment was used to quantifythe abilities of these treatments to shift the metabolic profiles ofindividuals with ASD to be more, or less, similar to those of the TDcohort. Without wishing to be bound by theory, an increase in Type IIerror, while undesirable in traditional hypothesis testing, is adesirable outcome in this particular analysis as the aim is to make thePDF of participants with ASD indistinguishable from the PDF of TDparticipants on the basis of their metabolic measurements.

FDA Model Identification from IMAGE Data. Referring now to FIGS. 4A-4B,a subset of five variables including methionine, cysteine, Cys-Gly,GSSG, and percent oxidized was selected then evaluated with FDA usingcross-validation. This model predicted the left-out samples with asensitivity of 88.0% and specificity of 90.2%, indicating very goodclassification accuracy. Investigation of each variable's contributionto the model's discriminant score revealed that the separation betweenthe ASD and TD cohorts was largely determined by glutathione precursorsand redox measurements (cysteine, Cys-Gly, GSSG, percent oxidized) withthe methylation metabolites (methionine) having a considerably smallereffect on the classification. Individuals' measurements for percentoxidized glutathione, in particular, were highly correlated with thediscriminant score output by the model.

To investigate individual variable contributions beyond the best model,the frequencies with which each of the fifteen measurements were used infive-variable models offering a fitted C-statistic of 0.96 or greaterwere considered (see FIG. 4B). This criterion was satisfied by 85 FDAmodels, out of a possible 3003 five-variable models overall, and it wasfound that the variables methionine, cysteine, and percent oxidized eachappeared in more than 84% of these models while no other measurement wasused in more than 32% of models. The measurement of percent oxidized,specifically, was used in almost 98% of the top models, reinforcing itsimportance for distinguishing participants in the ASD and TD cohorts.

Treatment Effect Sizes. The effect size for each treatment wascalculated as the median pre-to-post-treatment change in discriminantscore, with each participant's pre-treatment score paired with theirpost-treatment score. The distribution of the effect size was obtainedby bootstrap resampling, i.e. random sampling, with replacement, for asample set equal in size to the original set, with 10,000 replications,and the 0.025 and 0.975 quantiles of this bootstrap distributiondescribed the 95% confidence interval for the effect size.

Treatment Effects on Overall Metabolic Status. Referring now to FIG. 5,to assess the efficacies of each clinical treatment (MeCbl+LDFA, BH₄,HDFA, and placebo) to correct metabolic abnormalities in individualswith ASD, pre- and post-treatment observations from the four groups wereevaluated with the identified FDA model. PDFs of the resultingdiscriminant scores were estimated and compared to the ASD and TDdistributions generated from the IMAGE data. Without wishing to be boundby theory, the treatments producing the largest pre-to-post-treatmentshifts towards the TD distribution can be understood as those offeringthe greatest improvements to overall FOCM/TS metabolic status. Asdiscussed above and referring to Table 2 below, these shifts werequantified as the change in Type II error associated with the PDFs, withrespect to the null hypothesis Ho, brought about by each treatment.MeCbl+LDFA produced the largest increase in Type II error, followed byBH₄, indicating that these treatments were the most successful inaltering the FOCM/TS profiles of individuals with ASD to more closelyreflect those of TD individuals. Treatment with HDFA produced arelatively small increase in Type II error; however, due to the Type IIerror in this group being very large initially, the post-HDFA treatmenterror was actually the greatest among all treatments. As a result, the95% CI for the effect size of HDFA contained zero whereas the 95% CIsfor the other treatments did not contain zero. The 95% CI for theplacebo unexpectedly did not contain zero, indicating a small, butstatistically significant, metabolic shift in this group.

TABLE 2 Changes in Type II error associated with the PDFs ofdiscriminant scores before and after each treatment, with respect to thenull hypothesis H₀. Effect size was calculated as the median change inpre-to-post-treatment discriminant score, where pre-treatment sampleswere paired with their post-treatment data points. Change in Treat-Pre-treatment Post-treatment Type II Effect Size ment Type II Error TypeII Error Error (95% CI) MeCbl +  1.7% 43.6% +41.9% 0.89 (0.68, LDFA1.40) BH₄  0.3% 41.1% +40.8% 0.73 (0.31, 1.11) HDFA 32.2% 49.5% +17.2%0.17 (−0.21, 0.46) Placebo 15.1% 21.3% +6.20% 0.31 (0.12, 0.60)

Regression. Kernel partial least squares (KPLS), a nonlinear extensionof the partial least squares (PLS) algorithm, was used. KPLS regressionhandles noisy and collinear data well compared to ordinary least squaresand is a more appropriate choice when the number of observations issmall compared to the number of variables. The regression task beganwith the predictor variable set X (containing pre-to-post-treatmentchanges in FOCM/TS measurements) and the response variable set Y(containing pre-to-post-treatment changes in the VABS Composite). Toinitiate the PLS algorithm, a projection vector for the n samplescontained in X is determined and a separate projection vector for the nsamples contained in Y is identified. The projections of X and Y werethen used to calculate the regression coefficients for the model.Further projection directions can be found by subtracting thecontributions of the previous directions from X and Y.

KPLS regression first carried out a nonlinear transformation of the formF=Φ(X) on the predictor set, with the dimension of F typically muchlarger than that of X. The algorithm then proceeded in a modified formof linear PLS to identify the regression model for predicting Y from F,rather than from X. Gaussian kernel functions were used for thenonlinear transformation Φ(X). Here, X contained thepre-to-post-treatment changes of a subset of the measured metabolitesand Y described the pre-to-post-treatment change in the VABS Composite.FIG. 3B provides a summary of these predictor and response variablesused to develop the regression model.

Cross-Validation. Classification and regression analyses made use ofleave-one-out cross-validation to provide a statistically independentassessment of model predictions. This technique removes one sample fromthe data set, identifies the FDA or KPLS model that fits the remainingdata, and then uses the model to predict the sample that was removed.The sample is then replaced and the procedure repeated until all sampleshave been individually removed once. For classification, the confusionmatrix is then constructed using the cross-validated predictions insteadof the fitted discriminant scores. Similarly, the sum of squared errorsfor assessing a regression model was computed as the difference betweenthe measured and the predicted, rather than the fitted, values.Approaching the modeling tasks in this manner helps to alleviateconcerns of over-fitting that may arise during model development.

Prediction of Changes in Adaptive Behavior. Treatments with MeCbl+LDFA,BH₄, and HDFA offer varying levels of improvement in metabolic status inindividuals with ASD. As discussed above, the predictor variables in theregression were the pre-to-post-treatment changes in metabolicmeasurements while the response variable was the pre-to-post-treatmentchange in VABS Composite. All treatment groups, including the placebogroup, were included in the regression (74 samples in total) so as tocapture a range of biochemical/behavioral effects and to further guardagainst overfitting by using metabolites from as many participants aspossible. This analysis is independent of the treatment used as thechanges in the pre-to-post-treatment are correlated with changes in theVABS scores and information about the treatment itself is not used forregression. Without wishing to be bound by theory, the type of treatmentused affects the pre-to-post-treatment changes in the metabolites, butthe treatment information is may be implicitly and not explicitlyinvolved in this analysis.

All combinations of each number of variables were exhaustively testedand the R² from cross-validation was used as the evaluation criterionfor the regression. Comparing the maximum R² given by each number ofinput variables showed the model performance to decrease when more thansix variables were used. The highest cross-validated R² of 0.471 wasobtained using Δmethionine, ΔGlu-Cys, ΔCys-Gly, ΔtGSH, ΔtGSH/GSSG, andΔSGSH/GSSG as predictor variables in the regression, where A indicatesthe pre-to-post-treatment change of a particular metabolite ormetabolite ratio. The five top-performing models using six predictorvariables are listed in Table 3 below.

TABLE 3 The five combinations of predictor variables producing thehighest R2 from cross-validation with KPLS regression when using sixvariables. Variables R² ΔMethionine, ΔG1u-Cys, ΔCys-Gly, 0.471 ΔtGSH,ΔtGSH/GSSG, ΔfGSH/GSSG ΔMethionine, ΔSAM/SAH, ΔAdenosine, 0.470ΔCysteine, ΔCys-Gly, tGSH/GSSG ΔMethionine, ΔSAM, ΔSAM/SAH, 0.467ΔAdenosine, ΔCysteine, ΔCys-Gly ΔSAM, ΔSAM/SAH, ΔHomocysteine, 0.462ΔCysteine, ΔCys-Gly, ΔtGSH/GSSG ΔMethionine, ΔSAM/SAH, ΔAdenosine, 0.454ΔCys-Gly, ΔtGSH/GSSG, ΔfGSH/GSSG

Methods and systems of the present disclosure enable classification ofASD and TD individuals, as well as prediction of improvements inadaptive behavior in ASD individuals, based on metabolic data.Classification of ASD and TD individuals showed very good separationbetween these groups with a classifier sensitivity of 88.0% andspecificity of 90.2%. The methods and systems of the present disclosurego beyond univariate comparisons of individual measurements, and insteadconsider the combined contributions of multiple markers towards FOCM/TSmetabolic status. Including participants receiving the placebosubstantially increases the number of samples for model training andcross-validation and provides further safeguard against overfitting. Theuse of regression of changes in adaptive behavior and changes inbiochemical measurements offers insight into the metabolic andbehavioral improvements resulting from clinical treatment of individualswith ASD and allow care providers to monitor and adjust treatment ofthese individuals accordingly.

Although the disclosed subject matter has been described and illustratedwith respect to embodiments thereof, it should be understood by thoseskilled in the art that features of the disclosed embodiments can becombined, rearranged, etc., to produce additional embodiments within thescope of the invention, and that various other changes, omissions, andadditions may be made therein and thereto, without parting from thespirit and scope of the present invention.

What is claimed is:
 1. A system to predict changes in adaptive behaviorcomprising: at least one processor; and a non-transitory computerstorage media encoded with one or more computer programs executed by theat least one processor, the one or more computer programs configured to:perform a multivariate statistical analysis on one or more data sets oftypically developing (TD) individuals and one or more data sets ofautism spectrum disorder (ASD) individuals to produce a model thatclassifies TD individuals from ASD individuals, wherein the TD data setsinclude metabolic profile data for a plurality of TD individuals and ASDdata sets include pre-treatment metabolic profile data andpost-treatment metabolic profile data for patients receiving one or moreASD treatments; calculate pre-treatment discriminant scores andpost-treatment discriminant scores for a plurality of patients in theASD data sets; identify a change from the pre-treatment discriminantscore to the post-treatment discriminant score to quantify a treatmenteffect on metabolic profiles of the plurality of patients; perform atreatment effect regression analysis on changes in adaptive behaviorscores and the metabolic profiles of the plurality of patients; andquantify a predicted adaptive behavior score change for a target patientfrom a measured metabolic profile change in the target patient, whereinthe target patient is undergoing at least one of the one or more ASDtreatments.
 2. The system according to claim 1, wherein the metabolicprofile data includes data for folate-dependent one-carbon metabolism(FOCM) metabolites, transsulfuration (TS) pathway metabolites,methionine, SAM, SAH, SAM/SAH, 8-OHG, adenosine, homocysteine, cysteine,γ-L-glutamyl-L-cysteine (Glu.-Cys.), L-cysteine-L-glycine (Cys.-Gly.),tGSH, fGSH, GSSG, fGSH/GSSG, tGSH/GSSG, chlorotyrosine, nitrotyrosine,tyrosine, tryptophane, fCystine, fCysteine, fCystine/fCysteine, apercent of DNA methylation, a percent of oxidized glutathione, orcombinations thereof.
 3. The system according to claim 1, wherein themultivariate statistical analysis comprises performing a Fisherdiscriminant analysis.
 4. The system according to claim 1, wherein theone or more ASD treatments include MeCbl+LDFA, BH₄, HDFA, orcombinations thereof.
 5. The system according to claim 4, wherein theone or more ASD treatments include placebo treatments.
 6. The systemaccording to claim 1, wherein the adaptive behavior score includes aVineland Adaptive Behavior Scales (VABS) Composite score.
 7. The systemaccording to claim 1, wherein the treatment effect regression analysisincludes use of a kernel partial least squares algorithm.
 8. The systemaccording to claim 7, wherein the one or more computer programsconfigured to identify a change from the pre-treatment discriminantscore to the post-treatment discriminant score to quantify a treatmenteffect on metabolic profiles of the plurality of patients is furtherconfigured to: calculate, for the plurality of patients in the ASD datasets, a first probability that the model will classify a data setpatient as TD pre-treatment and a second probability that the model willclassify the data set patient as TD post-treatment; and identify achange from the first probability to the second probability.
 9. Thesystem according to claim 8, wherein an increase in probability from thefirst probability to the second probability identifies a positivetreatment effect.
 10. A computer implemented method to predict changesin adaptive behavior comprising: performing a multivariate statisticalanalysis on one or more data sets of typically developing (TD)individuals and one or more data sets of autism spectrum disorder (ASD)individuals to produce a model that classifies TD individuals from ASDindividuals, wherein the TD data sets include metabolic profile data fora plurality of TD individuals and ASD data sets include pre-treatmentmetabolic profile data and post-treatment metabolic profile data forpatients receiving one or more ASD treatments; calculating pre-treatmentdiscriminant scores and post-treatment discriminant scores for aplurality of patients in the ASD data sets; identifying a change fromthe pre-treatment discriminant score to the post-treatment discriminantscore to quantify a treatment effect on metabolic profiles of theplurality of patients; performing a treatment effect regression analysison changes in adaptive behavior scores and metabolic profiles of theplurality of patients; and quantifying a predicted adaptive behaviorscore change for a target patient from a measured metabolic profilechange in the target patient, wherein the target patient is undergoingat least one of the one or more ASD treatments.
 11. The method accordingto claim 10, wherein the metabolic profile data includes data forfolate-dependent one-carbon metabolism (FOCM) metabolites,transsulfuration (TS) pathway metabolites, methionine, SAM, SAH,SAM/SAH, 8-OHG, adenosine, homocysteine, cysteine,γ-L-glutamyl-L-cysteine (Glu.-Cys.), L-cysteine-L-glycine (Cys.-Gly.),tGSH, fGSH, GSSG, fGSH/GSSG, tGSH/GSSG, chlorotyrosine, nitrotyrosine,tyrosine, tryptophane, fCystine, fCysteine, fCystine/fCysteine, apercent of DNA methylation, a percent of oxidized glutathione, orcombinations thereof.
 12. The method according to claim 10, wherein themultivariate statistical analysis comprises performing a Fisherdiscriminant analysis.
 13. The method according to claim 10, wherein theone or more ASD treatments include MeCbl+LDFA, BH₄, HDFA, orcombinations thereof.
 14. The method according to claim 13, wherein theone or more ASD treatments include placebo treatments.
 15. The methodaccording to claim 10, wherein the adaptive behavior score includes aVineland Adaptive Behavior Scales (VABS) Composite score.
 16. The methodaccording to claim 10, wherein the treatment effect regression analysisincludes use of a kernel partial least squares algorithm.
 17. The methodaccording to claim 16, wherein identifying a change from thepre-treatment discriminant score to the post-treatment discriminantscore to quantify a treatment effect on metabolic profiles of theplurality of patients includes: calculating, for the plurality ofpatients in the ASD data sets, a first probability that the model willclassify a data set patient as TD pre-treatment and a second probabilitythat the model will classify the data set patient as TD post-treatment;and identifying a change from the first probability to the secondprobability.
 18. The method according to claim 17, wherein an increasein probability from the first probability to the second probabilityidentifies a positive treatment effect.
 19. A computer implementedmethod to predict changes in adaptive behavior comprising: performingFisher discriminant analysis on one or more data sets of typicallydeveloping (TD) individuals and one or more data sets of autism spectrumdisorder (ASD) individuals to produce a model that classifies TDindividuals from ASD individuals, wherein the TD data sets includefolate-dependent one-carbon metabolism (FOCM) and transsulfuration (TS)pathway metabolic profile data for a plurality of TD individuals and ASDdata sets include pre-treatment folate-dependent one-carbon metabolism(FOCM) and transsulfuration (TS) pathway metabolic profile data andpost-treatment folate-dependent one-carbon metabolism (FOCM) andtranssulfuration (TS) pathway metabolic profile data for patientsreceiving one or more ASD treatments; calculating, for the plurality ofpatients in the ASD data sets, a first probability that the model willclassify a data set patient as TD pre-treatment and a second probabilitythat the model will classify the data set patient as TD post-treatment;identifying a change from the first probability to the secondprobability to quantify a treatment effect on metabolic profiles of theplurality of patients; performing a treatment effect regression analysison changes in adaptive behavior scores and metabolic profiles of theplurality of patients; and quantifying a predicted adaptive behaviorscore change for a target patient from a measured metabolic profilechange in the target patient, wherein the target patient is undergoingat least one of the one or more ASD treatments.
 20. The method accordingto claim 19, wherein the treatment effect regression analysis includesuse of a kernel partial least squares algorithm.